In recent years, low-orbit satellites have become an important development direction in satellite IoT systems. The number of terminals is large and data collisions occur frequently in the low-orbit satellite IoT scenario. How to design a reliable random access protocol to improve the tolerance of the system for collision is one of the research hotspots in this field. In this paper, the random access protocol, used in the Internet of Things (IoT), for low-orbit satellites is studied, and the access process of the IoT terminals in the scenario is constructed. The access performance of the SSA protocol is analyzed and an improved SSA random access strategy, called Retransmission-SSA (R-SSA), is proposed. The simulation results show that the designed R-SSA can effectively tolerate the signal conflicts between terminals in the beam-hopping LEO IoT scenario and improve the probability of the concurrent access of low-orbit sIoT terminals.

Beam Hopping (BH) technology is adopted in satellite communication systems primarily to address the rigid resource allocation issue of traditional fixed beam coverage. By dynamically adjusting beam pointing and dwell time, it achieves efficient utilization and flexible allocation of resources. This paper addresses the beam hopping scheduling problem in Low-Earth-Orbit (LEO) satellite networks for high-priority and time-sensitive services. We establish a system gain maximization model and propose a heuristic beam scheduling algorithm (HBSA) that pre-schedules critical traffic and greedily allocates remaining resources under interference and fairness constraints. We further extend the framework with a graph reinforcement learning beam-hopping scheduling scheme (GRL-BHS) that captures structural network dependencies for near-optimal adaptive scheduling. Extensive simulations against seven benchmarks demonstrate that HBSA delivers near-optimal gain (within 2% of MWC and GRL-BHS), ensures high-priority satisfaction, maintains a Jain fairness index above 0.84 under heavy load, and reduces signaling overhead by an order of magnitude, while preserving a low polynomial complexity of O(T × J × K). GRL-BHS offers the highest gain at the cost of offline training. HBSA thus emerges as the most balanced and practically deployable solution, providing a solid foundation for future intelligent BH scheduling in mega constellations.

Beam-Hopping (BH) dynamically adjusts beam pointing and dwell time to overcome the rigid resource allocation of traditional fixed beam coverage. This paper addresses the BH scheduling problem in low-Earth-orbit (LEO) satellite networks for high-priority and time-sensitive services. A system-gain maximization model and a heuristic beam scheduling algorithm (HBSA) that pre-schedules critical traffic and greedily allocates remaining resources under interference and fairness constraints are proposed. Furthermore, this paper extends the framework with a graph reinforcement learning beam-hopping scheduling scheme (GRL-BHS) that captures structural network dependencies for near-optimal adaptive scheduling. Simulations against seven benchmarks show that the HBSA achieves near-optimal gain (within $2\%$ of MWC and GRL-BHS), ensures high-priority satisfaction, maintains a Jain fairness index above $0.84$ under heavy load, and reduces signaling overhead by an order of magnitude, while preserving a low polynomial complexity of $O(T\times J\times K+T\times K^2)$. GRL-BHS offers the highest gain at the cost of offline training, whereas HBSA is the most balanced and practically deployable solution, providing a solid foundation for future intelligent BH scheduling in mega constellations.